High-throughput determination of the sequences and amounts of multiple mRNAs from single cells is a critical, unsolved question in modern biology. Standard RT-PCR and NextGen sequencing analyze mRNAs from cell populations and thus collect only aggregate values, whereas biological function is often mediated via coordinate gene expression within single cells. The development of technologies for the analysis of single cells should be highly enabling for cell biology in general and tumor biology in particular. Tumors tend to be both individualized and evolving entities, with some mutations leading to metastasis while others provide valuable diagnostic and prognostic information. There is also a growing body of thought that suggests that observed heterogeneities within and between tumors may be due to the idiosyncratic differentiation of cancer stem cells. However, it has proven very difficult to fuly validate this hypothesis, in part because of the rarity of such stem cells, perhaps less than one cell in 105. In general, it is difficult to fully examine hypotheses relevant to single cell biolog because of the small numbers of cells that are typically used in single cell techniques (frequently less than 1,000). To overcome this problem, we have now developed a high-throughput method for the analysis of single cell transcriptomes that scales to upwards of a million cells. We will validate this technique by applying it to the analysis of multiple tumor and normal cell lines, and derive statistical measures of its goodness.